Information about a set of users of a social networking system is obtained to develop a predictive model of income distribution for all users of the social networking system. This predictive model is based on selected attributes about the users (e.g., declared/profile information, user historical information, and/or social information). Users of the social networking system are mapped to a specific income bracket based on statistical correlations derived from the predictive model. Advertisements are targeted to users based on income bracket. The system may use a machine learning algorithm to analyze conversion rates of targeted advertising to retrain the predictive model.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method comprising: receiving information about a subset of users of the social networking system, the information describing connections between users of the social networking system and actions taken by users on the social networking system; determining an income distribution model of the subset of users based on the received information; analyzing, by a computer processor, the income distribution model to normalize the received information; defining ranges of income brackets based upon the analysis of the income distribution model; determining, by a computer processor, one or more confidence metrics for the ranges of income brackets for a user of the subset of users, each confidence metric describing a likelihood that the user's income level falls within an associated income bracket; and determining a modifier for providing an advertisement to the user that is targeted to a particular income bracket based on the confidence metric associated with the particular income bracket.
2. The method of claim 1 , wherein the received information includes an estimated yearly income of each user of the subset of users of the social networking system.
3. The method of claim 1 , wherein the received information includes an estimated range of yearly income of each user of the subset of users of the social networking system.
4. The method of claim 1 , wherein the received information includes user profile information of each user of the subset of users of the social networking system.
5. The method of claim 1 , wherein the received information includes unstructured information gathered from activity on the social networking system about each user of the subset of users.
6. The method of claim 1 , wherein the received information includes, for each user of the subset of users of the social networking system, an analysis of posted content by the user that indicates a higher-than-average income potential as compared to other analyses of posted content by other users in the subset of users.
7. The method of claim 1 , wherein the received information includes, for each user of the subset of users of the social networking system, an analysis of posted content by the user that indicates a lower-than-average income potential as compared to other analyses of posted content by other users in the subset of users.
8. The method of claim 1 , wherein analyzing the income distribution model to normalize the received information further comprises: selecting a distribution model from known distribution models based on a curve fit of the received information; determining a margin of error based on the curve fit of the selected distribution model; and identifying bad outliers in the selected distribution model.
9. The method of claim 1 , wherein the actions taken by users on the social networking system are selected from a group consisting of: commenting on a photo album, communications between users, becoming a fan of a musician, adding an event to a calendar, or interactions with advertisements on the social networking system.
10. The method of claim 1 , wherein information describing connections between users of the social networking system are selected from a group consisting of: wall posts, comments on photos, geographic places where they have been tagged together, photos in which they have both been tagged in, or strength of the connection between users.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
September 13, 2012
December 3, 2013
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.